RAG Personal Assistant

Overview

I developed an advanced conversational AI system using Retrieval-Augmented Generation (RAG) that enables natural, context-aware interactions with document collections. This solution bridges the gap between static documentation and dynamic conversation, allowing users to extract precise information through simple questions.

Key Features

• Seamless Document Integration: Automatically processes and indexes multiple formats (PDF, DOCX, TXT, JSON) with minimal setup
• Semantic Search: Leverages Weaviate vector database to understand user intent and retrieve contextually relevant information
• Human-like Responses: Combines retrieved context with OpenAI's GPT models to generate accurate, conversational answers
• Production-Ready Architecture: Built with scalability in mind using Docker containerization
• Real-Time Communication: Implements WebSockets for responsive, chat-like experience with streaming responses

Technical Implementation

• Backend: FastAPI for high-performance async API endpoints
• AI Framework: LangChain for document processing and LLM orchestration
• Performance Optimization: Redis caching to minimize redundant processing
• Robust Error Handling: Graceful degradation with informative fallbacks
• Quality Assurance: Comprehensive test suite ensuring reliability under various conditions

Current Usage

I currently use this system as my personal knowledge assistant, providing it with my own documents, notes, and information about myself. It helps users quickly retrieve and interact with my personal knowledge base, from professional research to personal projects and interests. The system allows users to have natural conversations about my own information, making it significantly easier to recall details, generate insights from my own data, and maintain a searchable repository of my knowledge that understands the context of user queries.

Business Impact

This system significantly reduces the time users spend searching through documentation. It has practical applications in customer support, employee onboarding, and knowledge management systems where rapid information retrieval is critical.